Causal knowledge engineering: A case study from COVID-19 (preprint)

COVID-19 appeared abruptly in early 2020, requiring a rapid response amid a context of great uncertainty. Good quality data and knowledge was initially lacking, and many early models had to be developed with causal assumptions and estimations built in to supplement limited data, often with no reliable approach for identifying, validating and documenting these causal assumptions. Our team embarked on a knowledge engineering process to develop a causal knowledge base consisting of several causal BNs for diverse aspects of COVID-19. The unique challenges of the setting lead to experiments with the elicitation approach, and what emerged was a knowledge engineering method we call Causal Knowledge Engineering (CKE). The CKE provides a structured approach for building a causal knowledge base that can support the development of a variety of application-specific models. Here we describe the CKE method, and use our COVID-19 work as a case study to provide a detailed discussion and analysis of the method.

Modelling long COVID using Bayesian networks (preprint)

Motivated by the ambiguity of operational case definitions for long COVID and the impact of the lack of a common causal language on long COVID research, in early 2023 we began developing a research framework on this post-acute infection syndrome. We used directed acyclic graphs (DAGs) and Bayesian networks (BNs) to depict the hypothesised mechanisms of long COVID in an agnostic fashion. The DAGs were informed by the evolving literature and subsequently refined following elicitation workshops with domain experts. The workshops were structured online sessions guided by an experienced facilitator. The causal DAG aims to summarise the hypothesised pathobiological pathways from mild or severe COVID-19 disease to the development of pulmonary symptoms and fatigue over four different time points. The DAG was converted into a BN using qualitative parametrisation. These causal models aim to assist the identification of disease endotypes, as well as the design of randomised controlled trials and observational studies. The framework can also be extended to a range of other post-acute infection syndromes.

Understanding Parkinson's Disease Conversations on Chinese Social Media: Themes, Trends, and Connections (preprint)

Parkinson's disease profoundly impacts patients' quality of life, yet public awareness is limited. As social networks reach broad audiences, they are vital platforms for health communication. This study analyzed Parkinson's disease (PD) topics on China's major social network, Weibo, to reveal public awareness and concerns. We found Parkinson's became a popular keyword due to its association with hand tremors, though this trend may impede public comprehensive understanding of this disease. Through time series analysis, marketing accounts were found to publish misinformation through concentrated posts during specific periods, but this false information had limited impact. Further analysis of communication network structures showed people focused more on mutual aid than acquiring Parkinson's disease knowledge. This indicates advanced technique should be develop to promote accurate Parkinson's knowledge through social media. Additionally, the COVID-19 pandemic over the past three years led people to pay more attention to Parkinson's, demonstrating social media's role in responding to health crises. Overall, this study details how people discuss diseases on social media, providing a basis for developing communication strategies to promote Parkinson's and other diseases, while also informing patient mutual aid systems. To our best knowledge, this is the first study revealing the communications characteristics of PD related contents on social network. The findings may also empower more effective health promotion and patient support worldwide through social networks.

Analysis of blood and nasal epithelial transcriptomes to identify mechanisms associated with control of SARS-CoV-2 viral load in the upper respiratory tract (preprint)

Background: The amount of SARS-CoV-2 detected in the upper respiratory tract (URT viral load) is a key driver of transmission of infection. Current evidence suggests that mechanisms constraining URT viral load are different from those controlling lower respiratory tract viral load and disease severity. Understanding such mechanisms may help to develop treatments and vaccine strategies to reduce transmission. Combining mathematical modelling of URT viral load dynamics with transcriptome analyses we aimed to identify mechanisms controlling URT viral load. Methods: COVID-19 patients were recruited in Spain during the first wave of the pandemic. RNA sequencing of peripheral blood and targeted NanoString nCounter transcriptome analysis of nasal epithelium were performed and gene expression analysed in relation to paired URT viral load samples collected within 15 days of symptom onset. Proportions of major immune cells in blood were estimated from transcriptional data using computational differential estimation. Weighted correlation network analysis (adjusted for cell proportions) and fixed transcriptional repertoire analysis were used to identify associations with URT viral load, quantified as standard deviations (z-scores) from an expected trajectory over time. Results: Eighty-two subjects (50% female, median age 54 years (range 3-73)) with COVID-19 were recruited. Paired URT viral load samples were available for 16 blood transcriptome samples, and 17 respiratory epithelial transcriptome samples. Natural Killer (NK) cells were the only blood cell type significantly correlated with URT viral load z-scores (r = -0.62, P = 0.010). Twenty-four blood gene expression modules were significantly correlated with URT viral load z-score, the most significant being a module of genes connected around IFNA14 (Interferon Alpha-14) expression (r = -0.60, P = 1e-10). In fixed repertoire analysis, prostanoid-related gene expression was significantly associated with higher viral load. In nasal epithelium, only GNLY (granulysin) gene expression showed significant negative correlation with viral load. Conclusions: Correlations between the transcriptional host response and inter-individual variations in SARS-CoV-2 URT viral load, revealed many molecular mechanisms plausibly favouring or constraining viral load. Existing evidence corroborates many of these mechanisms, including likely roles for NK cells, granulysin, prostanoids and interferon alpha-14. Inhibition of prostanoid production, and administration of interferon alpha-14 may be attractive transmission-blocking interventions.

Cardiac Arrhythmia after COVID-19 Vaccination versus Non-COVID-19 Vaccination: A Systematic Review and Meta-Analysis (preprint)

Aims: Cardiac arrhythmia is a rare complication after vaccination. Recently, reports of arrhythmia after COVID-19 vaccination have increased. Whether the risk for cardiac arrhythmia is higher with COVID-19 vaccines than with non-COVID-19 vaccines remains controversial. This meta-analysis explored the incidence of arrhythmia after COVID-19 vaccination and compared it with the incidence of arrhythmia after non-COVID-19 vaccination. Methods: We searched the MEDLINE, Scopus, Cochrane Library, and Embase databases for English-language studies reporting the incidence of arrhythmia (the primary endpoint) after vaccination from January 1, 1947 to October 28, 2022. Secondary endpoints included incidence of tachyarrhythmia and all-cause mortality. Subgroup analyses were conducted to evaluate the incidence of arrhythmia by age (children [<18 years] versus adults [[≥]18 years]), vaccine type (mRNA COVID-19 vaccine versus non-mRNA COVID-19 vaccine; individual non-COVID-19 vaccines versus COVID-19 vaccine), and COVID-19 vaccine dose (first versus second versus third). Random-effects meta-analyses were performed, and the intrastudy risk for bias and the certainty of evidence were evaluated. This study was registered with PROSPERO (CRD42022365912). Results: The overall incidence of arrhythmia from 36 studies (1,528,459,662 vaccine doses) was 291.8 (95% CI 111.6-762.7) cases per million doses. The incidence of arrhythmia was significantly higher after COVID-19 vaccination (2263.4 [875.4-5839.2] cases per million doses; 830,585,553 doses, 23 studies) than after non-COVID-19 vaccination (9.9 [1.3-75.5] cases per million doses; 697,874,109 doses, 14 studies; P<0.01). Compared with COVID-19 vaccines, the influenza, pertussis, human papillomavirus, and acellular pertussis vaccines were associated with a significantly lower incidence of arrhythmia. The incidence of tachyarrhythmia was significantly higher after COVID-19 vaccination (4367.5 [1535.2-12,360.8] cases per million doses; 1,208,656 doses, 15 studies) than after non-COVID-19 vaccination (25.8 [4.5-149.4] cases per million doses; 179,822,553 doses, 11 studies; P<0.01). Arrhythmia was also more frequent after the third dose of COVID-19 vaccine (19,064.3 [5775.5-61,051.2] cases per million doses; 7968 doses, 3 studies) than after the first dose (3450.9 [988.2-11,977.6] cases per million doses; 41,714,762 doses, 12 studies; P=0.05) or second dose (2262.5 [2205.9-2320.7] cases per million doses; 34,540,749 doses, 10 studies; P<0.01). All-cause mortality was comparable between the COVID-19 and non-COVID-19 vaccination groups. Conclusions: The overall risk for arrhythmia after COVID-19 vaccination was relatively low, although it was higher in COVID-19 vaccine recipients than in non-COVID-19 vaccine recipients. This increased risk should be evaluated along with other important factors, such as the incidence of local outbreaks and the risk for arrhythmia due to COVID infection itself, when weighing the safety and efficacy of COVID-19 vaccines.

Isolation, bioassay and 3D-QSAR analysis of 8-isopentenyl flavonoids from Epimedium sagittatum Maxim. as PDE5A inhibitors (preprint)

Background: As known, inhibition of phosphodiesterase 5 (PDE5) has the therapeutic effect on male erectile dysfunction (ED), and the processed folium of Epimedium sagittatum Maxim. (PFES) characterized by 8-isopentenyl flavonoids is a famous herb for treating ED. However, the main flavonoids inhibitory activities, structure-activity relationship (SAR) and signaling pathway have been not systematically studied so that its pharmacodynamic mechanism is unclear.  Methods: We aimed to initially reveal the PFES efficacy mechanism for treating ED. For the first time, 6 main 8-isopentenyl flavonoids (1-6) from PFES were isolated and identified. Then based on HPLC detection, we proposed a novel method with superior applicability compared to traditional radioisotope assay to screen inhibitors among them. We further established three-dimensional quantitative structure-activity relationship (3D-QSAR) models through CoMFA and CoMSIA to analyze the SAR for those inhibitors.  Results: The results were verified by cellular effects of the screened flavonoids. Among 6 compounds, Icariin (1), 2-O"-rhamnosylicaridide II (2) and Baohuoside I (3) were identified with significant activities (IC50 = 8.275, 3.233, 5.473 mM). Then 3D-QSAR studies showed that the replacement of C8 with bulky steric groups as isopentenyl, C3 with positive charge groups and C4' with a hydrogen bond acceptor substituent could increase inhibitory effects. In contrast, the substitution of C7 with bulky steric groups or hydrophilic groups tended to decrease the efficacies. And compounds1, 2, 3 could increase cGMP level and decrease cytoplasmic Ca2+ of rat corpus cavernosum smooth muscle cells (CCSMCs)by activating PKG.  Conclusion: 8-isopentenyl flavonoids could be the main pharmacodynamic substances of PFES in the treatment for ED, and some had significant PDE5A1 inhibitory activities so as to activate cGMP/PKG/Ca2+ signaling pathway in CCSMCs, that was related to the substituents at the key sites such as C8, C3, C4' and C7 in the characteristic compounds.

Coethnic Concentration and Asians’ Perceived Discrimination across U.S. Counties during COVID-19

Aggregate figures unequivocally depict an increase in anti-Asian sentiment in the United States and other Western countries since the start of the COVID-19 pandemic, but there is limited understanding of the contexts under which Asians encounter discrimination. The authors examine how coethnic concentration shapes Asians’ experiences of discrimination across U.S. counties during COVID-19 and also assess whether county-level context (e.g., COVID-19 infection rates, unemployment rates) could help explain this relationship. The authors analyze the Understanding Coronavirus in America tracking survey, a nationally representative panel of American households, along with county-level contextual data. The authors find an n-shaped relationship between coethnic concentration and Asians’ perceived discrimination. This relationship is explained largely by county-level COVID-19 infection rates. Together, the context of medium Asian concentration and high COVID-19 cases created a particularly hostile environment for Asians during COVID-19.

Modeling COVID-19 disease processes by remote elicitation of causal Bayesian networks from medical experts (preprint)

BackgroundCOVID-19 is a new multi-organ disease, caused by the SARS-CoV-2 virus, resulting in considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. A better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have been developed for its pathophysiology. The viruss rapid and extensive spread and therapeutic responses made this particularly difficult. Initially, no large patient datasets were publicly available, and their data remains limited. The medical literature was flooded with unfiltered, technical and sometimes conflicting pre-review reports. Clinicians in many countries had little time for academic consultations, and in-person meetings were unsafe. Methods and FindingsIn early 2020, we began a major project to develop causal models of the pathophysiological processes underlying the diseases clinical manifestations. We used Bayesian network (BN) models, because they provide both powerful tools for calculation and clear maps of probabilistic causal influence between semantically meaningful variables, as directed acyclic graphs (DAGs). Hence, they can incorporate expert opinion and numerical data, and produce explainable results. Dynamic causal BNs, which represent successive "time slices" of the system, can capture feedback loops and long-term disease progression. To obtain the likely causal structures, we used extensive elicitation of expert opinion in structured online sessions. Centered in Australia, with its exceptionally low COVID-19 burden, we managed to obtain many consultation hours. Groups of clinical and other subject matter specialists, all independent volunteers, were enlisted to filter, interpret and discuss the literature and develop a current consensus. We aimed to capture the experts understanding, so we encouraged discussion and inclusion of theoretically salient latent (i.e., unobservable) variables, documented supporting literature while noting controversies, and allowed experts to propose mechanisms by extrapolation from other diseases. Intermediary experts with some combined expertise facilitated the exchange of knowledge to BN modelers and vice versa. Our method was iterative and incremental: we systematically refined and checked the group output with one-on-one follow-up meetings with the original and new experts to validate previous results. In total, 35 experts contributed 126 face-to-face hours, and could review our products. ConclusionsOur method demonstrates and describes an improved procedure for developing BNs via expert elicitation, which can be implemented rapidly by other teams modeling emergent complex phenomena. The results presented are two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology, with three anticipated applications: (i) making expert knowledge freely available in a readily understandable and updatable form; (ii) guiding design and analysis of observational and clinical studies, by identifying potential mediators, confounders, and modifiers of treatment effects; (iii) developing and validating parameterized automated tools for causal reasoning and decision support, in clinical and policy settings. We are currently developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases.

The popularity of distance learning among International students in Hungary

With the rapid development of economic globalization and information technology, distance learning makes students study without distance limitation. Nowadays, high-level education organizations are not only top students' privileges anymore, instead, everyone can get this opportunity due to distance learning. At the same time, studying at far-away organizations gradually comes true. Obviously, distance learning is influencing our life especially during the COVID-19 pandemic, which plays a pivotal role in education. However, it also has some disadvantages, such as the weak study motivation and study effect, and high requirement for devices. In this study, we examined some existing literature and our primary research will study the International students' attitudes and their expectations about distance learning in Hungary. The research purpose is to reveal the popularity of distance learning among International students and provide suggestions for future distance learning.

Multiplex Solid-Phase RPA Coupled CRISPR-Based Visual Detection of SARS-CoV-2 (preprint)

COVID-19 has challenged the world's public health and led to over 4.5 million deaths. A rapid, sensitive, and cost-effective point-of-care virus detection device is crucial to the control and surveillance of the contagious severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic. Here we demonstrate a solid phase isothermal recombinase polymerase amplification coupled CRISPR-based (spRPA-CRISPR) assay for on-chip multiplexed, sensitive, and visual COVID-19 DNA detection. By targeting the SARS-CoV-2 structure protein encoded genomes, two specific genes were simultaneously detected with the control sample without cross-interaction with other sequences. The endpoint signal can be directly visualized for rapid detection of COVID-19. The amplified target sequences were immobilized on the one-pot device surface and detected using the mixed Cas12a-crRNA collateral cleavage of reporter released fluorescent signal when specific genes were recognized. The system was tested with samples of a broad range of concentrations (20 to 2x105 copies) and showed analytical sensitivity down to 20 copies per reaction. Furthermore, a low-cost LED UV flashlight (~$12) was used to provide a visible SARS-CoV-2 detection signal of the spRPA-CRISPR assay which could be purchased online easily. Thus, our platform provides a sensitive and easy-to-read multiplexed gene detection method with the capacity to specifically identify low concentration genes. Similar CRISPR biosensor chips can support a broad range of applications such as HPV DNA detection, influenza SARS-CoV-2 multiplex detection, and other infectious disease testing assays.

Protocol for the clinical performance assessment of SARS - CoV - 2 antibody testing reagent

To date, the coronavirus disease 2019 (COVID-19) pandemic is impacting globally. COVID-19 is mainly diagnosed via viral nucleic acid testing, but with the disadvantages of unsatisfactory sensitivity and high requirements for expensive equipment and facility the operating settings. Compared with nucleic acid testing, antibody testing usually has advantages as wide popularization, convenient sample collection, easy to achieve high throughput. less workload, high reproducibility, and low cost, therefore it will be an efficient supplement for nucleic acid detection to confirm COVID-19. This protocol provided detailed design for the assessment of antibody testing reagent, including consideration for the study objectives, calculation of sample size, inclusion and exclusion criteria, blinding method, experimental specimen, ethical issues, study management and quality control, data management and statistical analysis. and results report and so on, aiming to assist the researchers to systematically assess the critical performance of antibody testing reagent prior to large-scale application of the antibody testing reagent, so that researchers could make reasonable choices among different antibody testing reagents according to their respective purposes.

Modelling upper respiratory viral load dynamics of SARS-CoV-2 (preprint)

The relationships between viral load, severity of illness, and transmissibility of virus, have been the subject of intense interest since the start of the COVID-19 pandemic. They are fundamental to understanding pathogenesis and devising better therapeutic and prevention strategies. In this report we present within-host modelling to examine the viral load dynamics observed in the upper respiratory tract, drawing upon 2172 serial measurements from 605 subjects, collected from 17 different studies. We developed a mechanistic within-host model to describe viral load dynamics and host response, and also contrasted simpler mixed-effects regression analysis of peak viral load and its subsequent decline. The inclusion of age, sex, or disease severity of the subjects did not appreciably improve the fit of either the mechanistic model or the regression. In future work, this model will be used to connect viral load dynamics to underlying host traits, to better understand these complex interactions.

Public emotion responses during COVID-19 in China on social media: An observational study

Abstract COVID-19 (Corona Virus Disease 2019) has attacked many countries around world and caused profound impacts on public life. The outbreak of pandemic and other relevant factors are considered to cause emotion responses of residents. And the emotion responses of individuals are crucial for the execution of the prevention and control measures. By analyzing the linguistic features of posts on social media, this study aims to explore the change of public emotion responses during COVID-19 in China. We sampled 22,423 Weibo users and collected their Weibo posts by provincial area each day from January 1st, 2020 to April 18th, 2020. Next, we extracted linguistic features from posts according to the emotion-related dictionary. Based on important news and information released by the national and international organizations of public health, we divided the period from January 1st, 2020 to April 18th, 2020 into four stages (the initial period, the outbreak period, the stable period, and the prevention and control period). Then we gathered linguistic features by stage. After that, ANOVA was performed to examine the differences among these four stages. The results showed that the frequencies of 11 word categories showed significant differences among four stages, including fear, disappointment, guilt, missing, anger, panic, blessing, faith, love, praise, and surprise. The uses of several negative emotion words, such as fear, disappointment, guilt, and anger, increased saliently in the outbreak period compared with the initial period. Besides, panic words decreased significantly in the prevention and control period compared with the outbreak period. However, missing words were used more in the prevention and control period than other three periods. Moreover, people expressed more faith words and less love words in the outbreak period than the initial periods. Besides, people used more blessing words in the outbreak period compared with the stable period and prevention and control period. And praise words were used more in the outbreak period and the stable period compared with the initial period. The frequency of surprise words was significantly low only in the initial period. This study contributed to the understanding of public emotion responses during COVID-19, and had implications for the evidence-based execution of prevention and control measures.

Bridging the gaps in test interpretation of SARS-CoV-2 through Bayesian network modelling (preprint)

Background In the absence of an established gold standard, an understanding of the testing cycle from individual exposure to test outcome report is required to guide the correct interpretation of SARS-CoV-2 reverse transcriptase real-time polymerase chain reaction (RT-PCR) results and optimise the testing processes. Bayesian network (BN) models have been used within healthcare to bring clarity to complex problems. We use this modelling approach to construct a comprehensive framework for understanding the real world predictive value of individual RT-PCR results. Methods We elicited knowledge from domain experts to describe the test process from viral exposure to interpretation of the laboratory test, through a facilitated group workshop. A preliminary model was derived based on the elicited knowledge, then subsequently refined, parameterised and validated with a second workshop and one-on-one discussions. Results Causal relationships elicited describe the interactions of multiple variables and their impact on a RT-PCR result. Some interactions are infrequently observable and accounted for across the testing cycle such as pre-testing factors, sample collector experience and RT-PCR platform. By setting the input variables as evidence for a given subject and preliminary parameterisation, three scenarios were simulated to demonstrate potential uses of the model. Conclusions The core value of this model is a deep understanding of the total testing cycle, bridging the gap between a persons true infection status and their test outcome. This model can be adapted to different settings, testing modalities and pathogens, adding much needed nuance to the interpretations of results.

Iota carrageenan and xylitol inhibit SARS-CoV-2 in Vero cell culture (preprint)

COVID-19 (coronavirus disease 2019) is a pandemic caused by SARS-CoV-2 (severe acute respiratory syndrome-coronavirus 2) infection affecting millions of persons around the world. There is an urgent unmet need to provide an easy-to-produce, affordable medicine to prevent transmission and provide early treatment for this disease. The nasal cavity and the rhinopharynx are the sites of initial replication of SARS-CoV-2. Therefore, a nasal spray may be a suitable dosage form for this purpose. The main objective of our study was to test the antiviral action of three candidate nasal spray formulations against SARS-CoV-2. We have found that iota-carrageenan in concentrations as low as 6 {micro}g/ mL inhibits SARS-CoV-2 infection in Vero cell cultures. The concentrations found to be active in vitro against SARS-CoV-2 may be easily achieved by the application of nasal sprays already marketed in several countries. Xylitol at a concentration of 5 % m/V has proved to be viricidal on its own and the association with iota-carrageenan may be beneficial, as well.

OxCOVID19 Database: a multimodal data repository for better understanding the global impact of COVID-19 (preprint)

Oxford COVID-19 Database (OxCOVID19 Database) is a comprehensive source of information related to the COVID-19 pandemic. This relational database contains time-series data on epidemiology, government responses, mobility, weather and more across time and space for all countries at the national level, and for more than 50 countries at the regional level. It is curated from a variety of (wherever available) official sources. Its purpose is to facilitate the analysis of the spread of SARS-CoV-2 virus and to assess the effects of non-pharmaceutical interventions to reduce the impact of the pandemic. Our database is a freely available, daily updated tool that provides unified and granular information across geographical regions.

Investigation on mechanism and active components of Shufeng Jiedu Capsule in treatment of COVID-19 based on network pharmacology and molecular docking

Objective: To explore the novel coronavirus disease 2019 (COVID-19) treatment mechanism and active ingredients of Shufeng Jiedu Capsule by network pharmacology and molecular docking. Methods: TCMSP databases were used to search the chemical composition and target of Shufeng Jiedu Capsule, which was composed of Isatidis Radix, Polygonum cuspidatum, Forsythia suspensa, Phragmitis Rhizoma, Patrinia, Verbena officinalis, Bupleurum chinense, and Glycyrrhiza uralensis. The Swiss target prediction database was used to remove the target with possibility of 0. The corresponding targets of the disease were searched in the GeneCards and OMIM databases with the key words of "coronavirus", "pneumonia", "cough", and "fever". Through the UniProt databases to correct the name of the target point, take the intersection of Shufeng Jiedu Capsule and the disease target point, then use the software of Cytoscape 3.7.2 to build the network of traditional Chinese medicine-compound-target for visualization, through DAVID databases to carry out the GO function enrichment analysis and KEGG pathway enrichment analysis, predict the interaction mechanism of the target, and draw the column and bubble chart for visualization. The novel coronavirus (SARS-CoV-2) 3CL hydrolase was then docking with all compounds and the first five compounds with the least binding energy were selected for docking with angiotensin-converting enzyme II (ACE2). Results: The traditional Chinese medicine-compound-target compound target network contains eight kinds of traditional Chinese medicine-compound-target, 157 compounds and 260 corresponding targets. The key targets were PTGS2, ESR1, AR, etc. There were 393 items in GO functional enrichment analysis (P < 0.05), and 139 signaling pathways in KEGG pathway enrichment analysis. Molecular docking results showed that SARS-CoV-2 3CL hydrolase and ACE2 binding energy of the five core compounds, including 6-(3-oxoindolin-2-ylidene) indolo [2,1-b] quinazolin-12-one, bicuculline, physciondiglucoside, dihydroverticillatine, and licoisoflavanone, was smaller than that of recommended chemical drugs, and the binding energy to ACE2 was similar to that of the recommended chemical drug. Conclusion: The compounds in Shufeng Jiedu Capsule can regulate the signaling pathway of human cytomegalovirus infection, Kaposi's sarcoma associated herpesvirus infection, IL-17 signaling pathway, small cell lung cancer, etc. to treat COVID-19 by binding with SARS-CoV-2 3CL hydrolase and ACE2.

Distribution Of Medical Institutions In Cold Regions Of China Based On Public Health Emergencies (preprint)

Background：Recently, investments in the construction of medical resources have been increasing annually China, and consequently, the allocation of these resources has improved. However, the outbreak of covid-19 in 2020 highlights the problems in the distribution of medical institutions. After the occurrence of public health emergencies, the joint action of different levels of medical and health institutions can bring the role of urban medical and health system into full play. Therefore, after a global public health emergency, the study of medical institution distribution needs to be reconsidered.Methods：With the continuous application and development of GIS (Geographic Information System), the application of GIS in civil planning is relatively mature, and research investigating distribution has been conducted in depth. Based on this foundation, this paper analyzes the factors impacting distribution, such as the transportation system, land use characteristics and personal factors, by a weighted spatial separation model of a representative city in a cold region in China. Results：The data were sorted, edited and visually processed through the constructed geodatabase to perform an analysis of the spatial distributions of the factors impacting the accessibility of medical institutions in the study area. A weighted spatial separation model was built and applied to comprehensively consider several factors affecting accessibility, the accessibility of these medical institutions is significantly impacted when the spatial population distribution is considered as a factor in the weighted spatial separation model.Conclusions：The accessibility of medical institutions in this representative cold city in China was comparatively analyzed in this paper through theoretical research, software computations/simulations and model analysis based on the GIS paradigm. This study will help optimize the layout of medical institutions and improve medical equality.Trial registration: An ethics review and approval for this study was not required according to the local legislation and institutional requirements.